apps/tools/health-coach/openclaw/skills/health-tracker/SKILL.md
Parse natural-language health check-ins and log them to ~/health_data.json for dashboard sync. Activate when the user mentions food, water, walking, exercise, yoga, breathing, sleep, or screens.
npx skillsauth add smartrus/claude-skills-and-apps health-trackerInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
3 of 9 scanners reported clean
Some scanners were skipped, did not run, or reported a non-clean status. Review each row below.
Parse health check-in messages and update ~/health_data.json via the exec tool.
Use this exact pattern — replace the arguments based on what was reported:
python3 -c "
import json,os,sys
f=os.path.expanduser('~/health_data.json')
data={}
if os.path.exists(f):
try:
with open(f) as fh: data=json.load(fh)
except (json.JSONDecodeError,ValueError): data={}
date=sys.argv[1]
day=data.setdefault(date,{'habits':{},'water':0,'notes':''})
for k,v in (p.split('=',1) for p in sys.argv[2:]):
if k=='water':
day['water']=max(day['water'],int(v))
if day['water']>=8: day['habits']['l1']=True
elif k=='notes': day['notes']=(day['notes']+' | '+v).strip(' | ')
else:
new_val=v.lower() in ('true','1','yes')
cur_val=day['habits'].get(k)
if new_val or not cur_val: day['habits'][k]=new_val
with open(f,'w') as fh: json.dump(data,fh,indent=2)
print('Updated',date,json.dumps(day))
" "$(date +%Y-%m-%d)" "ARGS_HERE"
Logged a salad with fish and a walk:
"$(date +%Y-%m-%d)" "n1=true" "n3=true" "n4=true" "e2=true" "water=4" "notes=Salmon spinach salad, post-lunch walk"
Morning check-in with water and walk:
"$(date +%Y-%m-%d)" "l2=true" "e1=true" "notes=Lemon water, 40 min morning walk"
Evening recap:
"$(date +%Y-%m-%d)" "e3=true" "l3=true" "l5=true" "water=8" "notes=Evening walk done, breathing exercise, no screens"
| ID | Habit | Common triggers | |----|-------|----------------| | n1 | Veggies first | "salad", "ate veggies", "greens first" | | n2 | No sugary drinks | "just water", "black coffee" | | n3 | Fish / omega-3 | "salmon", "tuna", "fish oil" | | n4 | Leafy greens 2+ meals | "spinach", "kale", "arugula" | | n5 | Eating window 10-12h | "stopped eating by 7" | | n6 | Low sodium | "cooked at home", "no processed food" | | n7 | Nuts | "almonds", "walnuts", "handful of nuts" | | e1 | Morning walk | "morning walk", "walked before work" | | e2 | Post-lunch walk | "walked after lunch" | | e3 | Post-dinner walk | "evening walk" | | e4 | Workout / yoga | "did yoga", "gym", "home workout" | | l1 | Met water goal | Derived: set true when water ≥ 8 (hardcoded in exec template; adjust if user's goal differs) | | l2 | Lemon water AM | "lemon water", "warm water this morning" | | l3 | Breathing exercise | "breathing", "meditation" | | l4 | Slept 7+ hours | "slept 8 hours", "good sleep" | | l5 | No screens before bed | "no phone tonight" |
development
Designs transparency, explainability, and auditability frameworks to ensure humans can meaningfully oversee and audit autonomous AI decisions. Produces trust architecture documents including explanation templates, logging requirements, override mechanisms, and confidence-calibration standards. Trigger on queries about AI trust, explainability frameworks, AI transparency, human oversight, AI auditability, explanation design, and trust architecture. Do NOT trigger on general AI/ML model building, AI ethics policy writing, UI/UX design without trust context, compliance auditing, or data privacy implementation.
development
Models virtual replicas of physical systems (factories, supply chains, infrastructure) to simulate real-world operations and define predictive maintenance schedules. Generates digital twin specifications, sensor mapping requirements, and simulation parameters for operational planning. Trigger on queries about digital twins, virtual replicas, predictive maintenance planning, simulation models, sensor mapping, and operational simulation. Do NOT trigger on general IoT device management, dashboard design, data visualization, supply chain analytics without simulation context, or hardware procurement.
testing
Analyzes team workflows, task dependencies, and context-switching patterns to dynamically reorganize work assignments that reduce mental fatigue and cognitive overhead. Models task complexity, attention cost of switches, and focus-time requirements to optimize human productivity. Trigger on queries about cognitive load, context switching, mental fatigue, workflow optimization, task reorganization, focus time, and attention management. Do NOT trigger on general project management, sprint planning, Jira/Linear ticket triage, team capacity planning without cognitive context, performance reviews, or process documentation.
development
Strictly audits frontend code, UI components, and design mockups against WCAG 2.2 AA standards. Identifies violations in color contrast, keyboard navigation, screen reader compatibility, ARIA attributes, focus management, and touch target sizing. Generates prioritized remediation reports with code fix suggestions. Trigger on queries about WCAG audits, accessibility audits, a11y checks, color contrast, screen reader compatibility, keyboard navigation, ARIA attributes, and accessibility remediation. Do NOT trigger on general UI/UX design feedback, visual design critique, performance optimization, SEO auditing, or cross-browser compatibility testing.